Stock price prediction on Indonesia stock market with the influence of external factors using recurrent neural network with attention mechanism

Abstract

In recent years, more businesses and individuals have started to rely on data as their decision-making factor. Past data has been very useful in making the next decision about business activities in many business sectors, such as in investing activities. Many people invest in stocks without capable knowledge to analyze a proper performance of the stocks as there are a lot of factors affecting the value of stocks. To predict the value of the stock close value, deep learning especially recurrent neural network is applicable to predict the stock price. Previous research only involves the stock price data and vanilla recurrent neural network. This research predicts the stock price with the influence of external factors using recurrent neural network with attention mechanism. A block each for LSTM and attention mechanism model is created and can be repeated several times as needed. The AALI stock price data chosen to represent one of the biggest sectors in Indonesia, with 10 external factors related to the stocks. The result shows that the additional method of using external data with feature selection and attention mechanism helps to improve the model performance in predicting the stock prices. With the right combination through tuning the model, a single block each for LSTM and attention mechanism giving the best performance of the model with average MAPE of 9.438 for validation and 17.593 for testing. Future research will be able to improve the model accuracy by involving other stock price data and more external data to get more relevant supporting data.